Cui Yuanyuan, Fan Rongrong, Cheng Yuxin, Sun An, Xu Zhoubing, Schwier Michael, Li Linfeng, Lin Shushen, Schoebinger Max, Xiao Yi, Liu Shiyuan
From the Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Siemens Healthineers, Princeton, NJ.
J Comput Assist Tomogr. 2024;48(6):998-1007. doi: 10.1097/RCT.0000000000001637. Epub 2024 Jul 30.
The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA).
A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed.
Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity ( r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm.
The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.
本研究旨在评估基于深度学习（DL）的自动去骨算法在颈椎计算机断层血管造影（CTA）实际临床应用中的后处理图像质量。
回顾性纳入2022年1月至2022年7月期间行颈椎CTA检查的100例患者（31例女性，年龄61.4±12.4岁）。使用了三种不同类型的扫描仪。将同侧颈段动脉分为10段。由两名放射科医生分别独立评估DL算法和传统算法在去骨和血管完整性方面的表现。比较两种算法在性能上的差异。评估评分者间和评分者内的一致性，并分析颈动脉狭窄程度与去骨及血管完整性等级之间的相关性。
在两侧大多数节段上，两种算法在去骨和血管完整性排名上存在显著差异。与DL算法相比，传统算法显示颈动脉狭窄程度与血管完整性之间的相关性更高（r = -0.264对r = -0.180）。发现DL算法的评分者间和评分者内一致性高于或等于传统算法。
颈椎CTA去骨的DL算法表现明显优于传统后处理方法，尤其是在解剖结构复杂且靠近骨骼的节段。
